Comprehensive analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue architecture context. Combines expression profiling with spatial coordinates to reveal tissue organization, cell-cell interactions, and spatially variable genes.
Create detailed user stories, acceptance criteria, and feature specs
Example: Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example: Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example: Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example: Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Submit your Claude Code skill and start earning
epx add mims-harvard/tooluniverse --skill tooluniverse-spatial-transcriptomicsCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-spatial-transcriptomicsExecute the skills CLI command in your project's root directory to begin installation:
epx add mims-harvard/tooluniverse --skill tooluniverse-spatial-transcriptomicsFetches tooluniverse-spatial-transcriptomics from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/tooluniverse-spatial-transcriptomics
Restart Cursor to activate tooluniverse-spatial-transcriptomics. Access via /tooluniverse-spatial-transcriptomicsin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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Keeps context tight: tooluniverse-spatial-transcriptomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
tooluniverse-spatial-transcriptomics has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: tooluniverse-spatial-transcriptomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: tooluniverse-spatial-transcriptomics is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: tooluniverse-spatial-transcriptomics is focused, and the summary matches what you get after install.
We added tooluniverse-spatial-transcriptomics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
tooluniverse-spatial-transcriptomics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added tooluniverse-spatial-transcriptomics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added tooluniverse-spatial-transcriptomics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
tooluniverse-spatial-transcriptomics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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